Group Predictions

Row

Win percentage for the week

Season Win Percentage

Games Correct

181

Games Picked

282

Number of predictions

66

Row

This Week’s Predictions
Game Prediction Winner Correct Correct Votes Correct Percent
1 Baltimore Ravens Kansas City Chiefs No 18 0.2727
2 Detroit Lions San Francisco 49ers No 27 0.4091

Individual Predictions

row

Individual Table

Individual Results
Week 21
Name Weekly # Correct Percent Weeks Picked Season Percent Adj Season Percent Season Trend
Week 1 Week 2 Week 3 Week 4 Week 5 Week 6 Week 7 Week 8 Week 9 Week 10 Week 11 Week 12 Week 13 Week 14 Week 15 Week 16 Week 17 Week 18 Week 19 Week 20 Week 21
John Plaster 8 12 8 10 NA NA 6 9 7 10 9 7 8 8 10 10 12 13 3 3 2 1.0 19 0.6126 0.5543
Karen Coleman 7 10 NA 10 8 9 4 9 13 11 9 12 8 6 10 8 14 7 3 3 2 1.0 20 0.6082 0.5792
Aubrey Conn 9 12 8 11 9 9 4 11 11 8 7 12 8 5 9 10 NA 9 3 3 2 1.0 20 0.6015 0.5729
James Small 8 8 13 9 8 10 8 10 12 6 10 9 5 7 9 8 11 11 3 2 2 1.0 21 0.5993 0.5993
Yiming Hu 9 10 8 12 7 9 6 9 10 8 10 NA 7 6 9 9 12 10 NA NA 2 1.0 18 0.5977 0.5123
Anthony Brinson 10 11 8 6 10 9 8 10 9 7 8 11 9 5 9 8 7 10 3 3 2 1.0 21 0.5780 0.5780
Robert Martin 10 9 6 NA 9 9 6 9 NA 5 9 9 6 8 9 7 NA 8 3 4 2 1.0 18 0.5424 0.4649
Stephen Woolwine 8 13 9 NA NA 9 NA 11 11 NA 10 12 9 NA NA 9 NA 12 4 4 1 0.5 14 0.6854 0.4569
William Schouviller 10 9 11 10 8 9 NA 13 10 9 9 10 10 6 11 10 12 10 3 3 1 0.5 20 0.6468 0.6160
George Sweet 9 11 10 12 7 10 10 NA 11 8 10 13 9 8 8 8 11 9 4 3 1 0.5 20 0.6466 0.6158
Chris Papageorge 11 11 11 10 8 9 5 11 12 8 8 NA 10 NA 10 9 NA 9 NA NA 1 0.5 16 0.6356 0.4843
Jason Schattel 7 10 9 11 9 10 3 13 12 9 10 12 9 6 10 11 NA 11 3 3 1 0.5 20 0.6353 0.6050
Cheryl Brown 10 12 11 9 6 9 6 10 8 9 8 12 8 8 11 11 11 11 3 3 1 0.5 21 0.6277 0.6277
Anthony Bloss 8 10 11 12 10 10 5 9 9 8 9 11 10 6 11 9 13 10 2 3 1 0.5 21 0.6277 0.6277
Gabriel Quinones 9 11 12 12 6 9 6 11 NA 8 9 NA 9 8 9 10 NA 9 5 3 1 0.5 18 0.6229 0.5339
Stephen Bush 7 10 10 9 7 10 6 12 NA 5 10 11 8 8 11 9 14 11 4 3 1 0.5 20 0.6194 0.5899
Montee Brown 7 NA NA 9 9 11 6 12 11 8 10 12 8 6 11 10 10 9 2 4 1 0.5 19 0.6190 0.5600
Patrick Tynan 8 8 10 11 7 NA 5 11 10 7 11 13 8 5 12 10 12 9 4 3 1 0.5 20 0.6180 0.5886
David Plate 8 NA 8 9 8 10 5 9 11 8 9 12 NA 7 13 NA 11 9 4 4 1 0.5 18 0.6160 0.5280
Cody Koerwitz 7 9 11 12 7 10 6 NA 9 9 10 10 9 6 13 NA NA 10 1 3 1 0.5 18 0.6111 0.5238
Ryan Cvik 11 11 9 13 6 10 8 8 6 8 10 10 8 9 9 9 11 8 5 2 1 0.5 21 0.6099 0.6099
PABLO BURGOSRAMOS 9 11 10 12 7 12 6 8 9 7 10 NA 8 3 12 10 11 9 3 4 1 0.5 20 0.6090 0.5800
James Tierney 9 10 NA 10 10 12 7 10 8 9 9 10 8 8 7 11 8 10 4 2 1 0.5 20 0.6082 0.5792
Wayne Schofield 12 9 7 NA 8 NA 5 10 7 NA 10 NA 8 8 12 NA NA 12 3 2 1 0.5 15 0.6032 0.4309
Matthew Schultz 8 NA 10 8 9 9 6 10 11 8 9 12 5 NA NA NA 10 10 2 4 1 0.5 17 0.6027 0.4879
Michael Moss 10 NA 11 13 7 9 4 10 9 8 9 10 8 5 10 11 10 NA 2 3 1 0.5 19 0.6000 0.5429
Paul Shim 10 9 10 11 7 9 4 10 10 8 11 10 8 8 9 8 11 8 2 3 1 0.5 21 0.5922 0.5922
Jonathon Leslein 9 9 9 9 7 11 5 9 8 10 10 NA 9 5 10 9 10 13 2 2 1 0.5 20 0.5902 0.5621
Brian Hollmann 8 13 8 9 8 9 6 13 8 8 8 12 6 5 11 10 8 9 2 3 1 0.5 21 0.5851 0.5851
Steven Curtis NA NA 11 7 8 10 6 7 8 7 7 11 7 8 11 11 NA NA 4 2 1 0.5 17 0.5780 0.4679
Steven Webster 8 8 6 8 9 8 6 10 10 8 10 NA 7 6 12 NA NA NA NA 4 1 0.5 16 0.5708 0.4349
Gregory Flint 6 11 NA 11 8 10 NA NA 9 5 8 NA 9 5 10 NA 10 NA 2 NA 1 0.5 14 0.5615 0.3743
Trevor Macgavin 6 10 8 NA 6 7 4 NA 6 6 9 13 7 9 8 9 10 12 5 3 1 0.5 19 0.5560 0.5030
Justin Thrift 9 8 9 8 9 7 5 11 7 6 10 NA 7 9 8 10 NA 8 2 3 1 0.5 19 0.5480 0.4958
Rafael Torres 6 8 12 11 NA NA 6 NA 9 5 10 8 5 6 11 6 12 6 3 4 1 0.5 18 0.5443 0.4665
Cherylynn Vidal 10 9 9 12 9 7 4 6 9 7 NA 9 6 5 9 10 NA 8 NA 2 1 0.5 18 0.5366 0.4599
Melissa Printup 8 NA 8 7 10 7 6 NA NA 5 9 9 NA 9 7 8 8 9 4 3 1 0.5 17 0.5291 0.4283
Ryan Shipley 3 8 7 6 6 7 5 10 9 6 9 NA 5 6 11 8 9 7 3 3 1 0.5 20 0.4850 0.4619
Justin Crick 11 11 11 13 8 11 4 11 11 8 9 12 9 8 11 9 11 9 3 3 0 0.0 21 0.6489 0.6489
Ramar Williams NA 11 11 9 8 8 6 12 NA 8 NA 13 9 6 11 NA 13 9 4 3 0 0.0 17 0.6351 0.5141
Antonio Mitchell 10 12 NA 11 10 10 5 12 9 NA 10 12 NA 6 8 10 10 9 4 3 0 0.0 18 0.6266 0.5371
Vincent Scannelli 11 11 8 11 7 NA 5 9 12 10 10 NA 8 6 NA 11 NA NA 2 3 0 0.0 16 0.6108 0.4654
Brian Patterson 10 10 8 11 7 11 5 10 10 8 11 12 7 6 9 8 13 10 2 3 0 0.0 21 0.6064 0.6064
Eric Hahn 9 13 7 9 8 10 6 9 10 6 11 12 9 6 10 8 12 10 2 2 0 0.0 21 0.5993 0.5993
MICHAEL BRANSON 8 11 10 12 9 10 4 11 10 7 8 NA 10 9 8 8 NA 9 3 2 0 0.0 19 0.5960 0.5392
Terry Hardison 10 10 9 11 7 9 4 11 9 10 9 11 8 7 11 8 11 7 3 3 0 0.0 21 0.5957 0.5957
Pamela Augustine 11 13 6 9 6 9 5 10 9 NA 10 11 8 6 11 9 NA NA NA 4 0 0.0 17 0.5957 0.4822
Paul Presti 9 10 12 9 8 9 5 8 NA 9 9 NA 8 10 11 9 NA 8 3 3 0 0.0 18 0.5932 0.5085
Earl Dixon 9 11 8 12 5 NA 7 8 9 8 9 12 8 6 11 10 NA 9 3 NA 0 0.0 18 0.5870 0.5031
Bunnaro Sun 9 10 9 8 9 9 6 9 11 8 10 10 8 5 12 NA 9 10 3 1 0 0.0 20 0.5865 0.5586
Walter Archambo 7 10 10 11 7 9 5 9 12 NA 8 11 9 5 10 10 11 9 3 1 0 0.0 20 0.5858 0.5579
Daniel Baller 6 12 11 9 8 9 3 10 8 9 10 9 8 9 9 9 9 10 4 3 0 0.0 21 0.5851 0.5851
Ronald Schmidt 11 13 11 8 8 11 5 9 8 8 7 NA 7 7 9 11 10 NA 1 2 0 0.0 19 0.5840 0.5284
Daniel Major 8 13 6 7 8 11 7 11 NA NA 9 NA 7 NA NA NA NA 7 5 3 0 0.0 14 0.5829 0.3886
Shawn Carden 9 12 6 9 8 9 5 10 9 8 9 12 7 6 10 11 10 7 3 4 0 0.0 21 0.5816 0.5816
Kevin Kehoe 9 10 11 12 7 8 6 10 7 8 8 8 NA 6 9 8 12 9 3 4 0 0.0 20 0.5762 0.5488
Brandon Parks 8 8 NA NA 9 9 5 9 9 9 8 10 10 10 9 9 NA 8 NA NA 0 0.0 16 0.5752 0.4382
Thomas Brenstuhl 10 NA 8 8 8 9 5 9 11 6 11 NA 8 5 11 NA NA 9 3 4 0 0.0 17 0.5734 0.4642
Daniel Kuehl 6 10 8 11 7 9 7 12 7 6 10 11 8 6 9 9 NA 9 3 3 0 0.0 20 0.5677 0.5407
Khalil Ibrahim 7 12 9 NA 7 10 6 10 9 5 7 11 5 7 11 11 NA 9 3 NA 0 0.0 18 0.5650 0.4843
Kristen White 7 13 8 11 6 7 7 10 8 6 10 7 8 7 8 NA 13 8 2 2 0 0.0 20 0.5564 0.5299
George Mancini 7 12 10 10 9 10 6 NA 7 9 9 11 5 7 NA 10 7 6 1 3 0 0.0 19 0.5560 0.5030
Robert Lynch 9 9 6 10 10 6 4 9 10 5 9 8 7 6 12 10 11 8 4 NA 0 0.0 20 0.5504 0.5242
Min Choi 6 7 9 11 7 10 5 13 7 5 NA NA NA NA NA NA NA NA 4 4 0 0.0 13 0.5500 0.3405
Derrick Elam 6 9 11 10 10 7 NA 5 7 7 6 NA 7 9 NA 12 NA 11 2 1 0 0.0 17 0.5430 0.4396
Thomas Mccoy 8 10 9 7 8 9 7 11 7 7 NA 10 5 8 NA 9 9 8 3 1 0 0.0 19 0.5397 0.4883
Michael Edmunds 10 12 10 10 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA 0.0 4 0.6774 0.1290
Kevin O'NEILL 8 11 11 13 7 NA NA 10 NA NA NA NA NA NA NA NA NA NA NA NA NA 0.0 6 0.6522 0.1863
Shelly Bailey 9 10 NA 10 8 11 6 NA 13 7 9 13 NA NA NA NA NA NA NA 3 NA 0.0 11 0.6513 0.3412
Ryan Wiggins 8 11 11 12 7 11 5 11 10 8 10 10 7 6 12 10 NA 12 3 NA NA 0.0 18 0.6308 0.5407
Sarah Sweet 9 12 12 9 8 NA 6 11 11 10 8 9 6 NA NA NA NA NA NA NA NA 0.0 12 0.6307 0.3604
Daniel Halse 8 9 10 NA NA NA 7 11 NA 7 7 NA 8 NA 11 10 13 12 NA NA NA 0.0 12 0.6278 0.3587
Carlos Caceres 10 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA 0.0 1 0.6250 0.0298
Bradley Hobson 8 10 11 12 8 11 4 NA 8 9 9 12 NA 6 10 NA 11 NA 4 NA NA 0.0 15 0.6186 0.4419
Shaun Dahl 8 8 10 10 7 9 5 13 9 8 NA NA 8 8 13 11 NA 10 4 NA NA 0.0 16 0.6130 0.4670
Keithon Corpening 8 NA NA NA NA NA NA 11 12 9 8 10 6 8 12 9 10 8 NA 3 NA 0.0 13 0.6129 0.3794
Donald Park 8 12 7 9 NA NA 6 10 11 NA 9 NA NA NA NA NA NA NA NA NA NA 0.0 8 0.6050 0.2305
Amy Asberry 8 9 10 9 9 8 5 10 6 9 7 10 9 7 12 11 12 10 3 3 NA 0.0 20 0.5964 0.5680
James Blejski 8 11 10 14 NA 9 7 12 7 6 9 9 9 6 7 9 NA NA NA NA NA 0.0 15 0.5938 0.4241
Robert Gelo 6 9 10 10 9 11 5 11 6 9 9 10 8 6 11 NA NA NA 3 NA NA 0.0 16 0.5833 0.4444
William Sherman 8 11 10 10 6 NA 5 NA 9 NA 9 NA NA NA NA NA NA NA NA NA NA 0.0 8 0.5812 0.2214
Charlene Redmer 9 9 NA 9 9 11 NA 10 8 7 8 NA 6 NA NA 10 NA 9 3 NA NA 0.0 13 0.5806 0.3594
Rahmatullah Sharifi 11 9 8 11 8 8 5 NA NA NA NA NA NA NA NA NA NA NA NA NA NA 0.0 7 0.5769 0.1923
Manuel Vargas 10 9 11 12 7 10 6 12 5 5 7 8 9 7 10 NA 11 7 3 NA NA 0.0 18 0.5731 0.4912
Kevin Green 9 12 9 9 8 9 7 NA NA 6 10 11 4 7 6 8 13 9 3 NA NA 0.0 17 0.5691 0.4607
Jamal Willis 8 10 NA NA NA NA NA 9 NA NA NA NA NA NA NA NA NA NA NA NA NA 0.0 3 0.5625 0.0804
Jason James 9 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA 0.0 1 0.5625 0.0268
TYREE BUNDY 8 8 NA NA NA NA NA NA NA NA NA NA NA NA NA NA 11 NA NA NA NA 0.0 3 0.5625 0.0804
Michael Beck 9 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA 0.0 1 0.5625 0.0268
Alexander Santillan 5 NA 8 9 5 11 6 11 8 9 7 9 8 8 NA NA NA NA NA NA NA 0.0 13 0.5474 0.3389
Derrick Zantt 11 6 7 NA 6 9 6 11 NA NA NA NA NA NA NA NA NA NA NA NA NA 0.0 7 0.5385 0.1795
Rodney Cathcart NA NA NA NA NA NA NA NA NA NA NA NA 7 NA NA NA NA NA NA NA NA 0.0 1 0.5385 0.0256
David Spielman 8 NA 11 NA NA NA 3 NA 7 8 9 NA NA NA NA 8 NA 8 NA NA NA 0.0 8 0.5299 0.2019
Craig Webster NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA 8 NA NA NA 0.0 1 0.5000 0.0238
Edward Ford 6 8 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA 0.0 2 0.4375 0.0417

Individual Plots

Season Leaderboard

Season Leaderboard (Season Percent)
Week 21
Season Rank Name Donuts Won Weeks Picked Season Percent Adj Season Percent Season Trend
1 Stephen Woolwine 2 14 0.6854 0.4569
2 Michael Edmunds 0 4 0.6774 0.1290
3 Kevin O'NEILL 0 6 0.6522 0.1863
4 Shelly Bailey 2 11 0.6513 0.3412
5 Justin Crick 0 21 0.6489 0.6489
6 William Schouviller 2 20 0.6468 0.6160
7 George Sweet 2 20 0.6466 0.6158
8 Chris Papageorge 1 16 0.6356 0.4843
9 Jason Schattel 1 20 0.6353 0.6050
10 Ramar Williams 1 17 0.6351 0.5141
11 Ryan Wiggins 0 18 0.6308 0.5407
12 Sarah Sweet 0 12 0.6307 0.3604
13 Daniel Halse 0 12 0.6278 0.3587
14 Anthony Bloss 2 21 0.6277 0.6277
14 Cheryl Brown 0 21 0.6277 0.6277
16 Antonio Mitchell 1 18 0.6266 0.5371
17 Carlos Caceres 0 1 0.6250 0.0298
18 Gabriel Quinones 1 18 0.6229 0.5339
19 Stephen Bush 1 20 0.6194 0.5899
20 Montee Brown 1 19 0.6190 0.5600
21 Bradley Hobson 0 15 0.6186 0.4419
22 Patrick Tynan 2 20 0.6180 0.5886
23 David Plate 2 18 0.6160 0.5280
24 Shaun Dahl 2 16 0.6130 0.4670
25 Keithon Corpening 0 13 0.6129 0.3794
26 John Plaster 2 19 0.6126 0.5543
27 Cody Koerwitz 1 18 0.6111 0.5238
28 Vincent Scannelli 0 16 0.6108 0.4654
29 Ryan Cvik 1 21 0.6099 0.6099
30 PABLO BURGOSRAMOS 2 20 0.6090 0.5800
31 James Tierney 2 20 0.6082 0.5792
31 Karen Coleman 4 20 0.6082 0.5792
33 Brian Patterson 1 21 0.6064 0.6064
34 Donald Park 0 8 0.6050 0.2305
35 Wayne Schofield 1 15 0.6032 0.4309
36 Matthew Schultz 1 17 0.6027 0.4879
37 Aubrey Conn 1 20 0.6015 0.5729
38 Michael Moss 0 19 0.6000 0.5429
39 Eric Hahn 2 21 0.5993 0.5993
39 James Small 2 21 0.5993 0.5993
41 Yiming Hu 1 18 0.5977 0.5123
42 Amy Asberry 0 20 0.5964 0.5680
43 MICHAEL BRANSON 1 19 0.5960 0.5392
44 Pamela Augustine 2 17 0.5957 0.4822
44 Terry Hardison 0 21 0.5957 0.5957
46 James Blejski 1 15 0.5938 0.4241
47 Paul Presti 1 18 0.5932 0.5085
48 Paul Shim 1 21 0.5922 0.5922
49 Jonathon Leslein 1 20 0.5902 0.5621
50 Earl Dixon 0 18 0.5870 0.5031
51 Bunnaro Sun 0 20 0.5865 0.5586
52 Walter Archambo 0 20 0.5858 0.5579
53 Brian Hollmann 2 21 0.5851 0.5851
53 Daniel Baller 0 21 0.5851 0.5851
55 Ronald Schmidt 1 19 0.5840 0.5284
56 Robert Gelo 0 16 0.5833 0.4444
57 Daniel Major 2 14 0.5829 0.3886
58 Shawn Carden 1 21 0.5816 0.5816
59 William Sherman 0 8 0.5812 0.2214
60 Charlene Redmer 0 13 0.5806 0.3594
61 Anthony Brinson 2 21 0.5780 0.5780
61 Steven Curtis 0 17 0.5780 0.4679
63 Rahmatullah Sharifi 0 7 0.5769 0.1923
64 Kevin Kehoe 1 20 0.5762 0.5488
65 Brandon Parks 2 16 0.5752 0.4382
66 Thomas Brenstuhl 2 17 0.5734 0.4642
67 Manuel Vargas 0 18 0.5731 0.4912
68 Steven Webster 1 16 0.5708 0.4349
69 Kevin Green 0 17 0.5691 0.4607
70 Daniel Kuehl 0 20 0.5677 0.5407
71 Khalil Ibrahim 0 18 0.5650 0.4843
72 Jamal Willis 0 3 0.5625 0.0804
72 Jason James 0 1 0.5625 0.0268
72 Michael Beck 0 1 0.5625 0.0268
72 TYREE BUNDY 0 3 0.5625 0.0804
76 Gregory Flint 0 14 0.5615 0.3743
77 Kristen White 1 20 0.5564 0.5299
78 George Mancini 0 19 0.5560 0.5030
78 Trevor Macgavin 2 19 0.5560 0.5030
80 Robert Lynch 1 20 0.5504 0.5242
81 Min Choi 2 13 0.5500 0.3405
82 Justin Thrift 0 19 0.5480 0.4958
83 Alexander Santillan 0 13 0.5474 0.3389
84 Rafael Torres 1 18 0.5443 0.4665
85 Derrick Elam 2 17 0.5430 0.4396
86 Robert Martin 2 18 0.5424 0.4649
87 Thomas Mccoy 0 19 0.5397 0.4883
88 Derrick Zantt 0 7 0.5385 0.1795
88 Rodney Cathcart 0 1 0.5385 0.0256
90 Cherylynn Vidal 0 18 0.5366 0.4599
91 David Spielman 0 8 0.5299 0.2019
92 Melissa Printup 1 17 0.5291 0.4283
93 Craig Webster 0 1 0.5000 0.0238
94 Ryan Shipley 0 20 0.4850 0.4619
95 Edward Ford 0 2 0.4375 0.0417

Adjusted Season Leaderboard

Season Leaderboard (Adjusted Season Percent)
Week 21
Season Rank Name Donuts Won Weeks Picked Season Percent Adj Season Percent Season Trend
1 Justin Crick 0 21 0.6489 0.6489
2 Anthony Bloss 2 21 0.6277 0.6277
2 Cheryl Brown 0 21 0.6277 0.6277
4 William Schouviller 2 20 0.6468 0.6160
5 George Sweet 2 20 0.6466 0.6158
6 Ryan Cvik 1 21 0.6099 0.6099
7 Brian Patterson 1 21 0.6064 0.6064
8 Jason Schattel 1 20 0.6353 0.6050
9 Eric Hahn 2 21 0.5993 0.5993
9 James Small 2 21 0.5993 0.5993
11 Terry Hardison 0 21 0.5957 0.5957
12 Paul Shim 1 21 0.5922 0.5922
13 Stephen Bush 1 20 0.6194 0.5899
14 Patrick Tynan 2 20 0.6180 0.5886
15 Brian Hollmann 2 21 0.5851 0.5851
15 Daniel Baller 0 21 0.5851 0.5851
17 Shawn Carden 1 21 0.5816 0.5816
18 PABLO BURGOSRAMOS 2 20 0.6090 0.5800
19 James Tierney 2 20 0.6082 0.5792
19 Karen Coleman 4 20 0.6082 0.5792
21 Anthony Brinson 2 21 0.5780 0.5780
22 Aubrey Conn 1 20 0.6015 0.5729
23 Amy Asberry 0 20 0.5964 0.5680
24 Jonathon Leslein 1 20 0.5902 0.5621
25 Montee Brown 1 19 0.6190 0.5600
26 Bunnaro Sun 0 20 0.5865 0.5586
27 Walter Archambo 0 20 0.5858 0.5579
28 John Plaster 2 19 0.6126 0.5543
29 Kevin Kehoe 1 20 0.5762 0.5488
30 Michael Moss 0 19 0.6000 0.5429
31 Daniel Kuehl 0 20 0.5677 0.5407
31 Ryan Wiggins 0 18 0.6308 0.5407
33 MICHAEL BRANSON 1 19 0.5960 0.5392
34 Antonio Mitchell 1 18 0.6266 0.5371
35 Gabriel Quinones 1 18 0.6229 0.5339
36 Kristen White 1 20 0.5564 0.5299
37 Ronald Schmidt 1 19 0.5840 0.5284
38 David Plate 2 18 0.6160 0.5280
39 Robert Lynch 1 20 0.5504 0.5242
40 Cody Koerwitz 1 18 0.6111 0.5238
41 Ramar Williams 1 17 0.6351 0.5141
42 Yiming Hu 1 18 0.5977 0.5123
43 Paul Presti 1 18 0.5932 0.5085
44 Earl Dixon 0 18 0.5870 0.5031
45 George Mancini 0 19 0.5560 0.5030
45 Trevor Macgavin 2 19 0.5560 0.5030
47 Justin Thrift 0 19 0.5480 0.4958
48 Manuel Vargas 0 18 0.5731 0.4912
49 Thomas Mccoy 0 19 0.5397 0.4883
50 Matthew Schultz 1 17 0.6027 0.4879
51 Chris Papageorge 1 16 0.6356 0.4843
51 Khalil Ibrahim 0 18 0.5650 0.4843
53 Pamela Augustine 2 17 0.5957 0.4822
54 Steven Curtis 0 17 0.5780 0.4679
55 Shaun Dahl 2 16 0.6130 0.4670
56 Rafael Torres 1 18 0.5443 0.4665
57 Vincent Scannelli 0 16 0.6108 0.4654
58 Robert Martin 2 18 0.5424 0.4649
59 Thomas Brenstuhl 2 17 0.5734 0.4642
60 Ryan Shipley 0 20 0.4850 0.4619
61 Kevin Green 0 17 0.5691 0.4607
62 Cherylynn Vidal 0 18 0.5366 0.4599
63 Stephen Woolwine 2 14 0.6854 0.4569
64 Robert Gelo 0 16 0.5833 0.4444
65 Bradley Hobson 0 15 0.6186 0.4419
66 Derrick Elam 2 17 0.5430 0.4396
67 Brandon Parks 2 16 0.5752 0.4382
68 Steven Webster 1 16 0.5708 0.4349
69 Wayne Schofield 1 15 0.6032 0.4309
70 Melissa Printup 1 17 0.5291 0.4283
71 James Blejski 1 15 0.5938 0.4241
72 Daniel Major 2 14 0.5829 0.3886
73 Keithon Corpening 0 13 0.6129 0.3794
74 Gregory Flint 0 14 0.5615 0.3743
75 Sarah Sweet 0 12 0.6307 0.3604
76 Charlene Redmer 0 13 0.5806 0.3594
77 Daniel Halse 0 12 0.6278 0.3587
78 Shelly Bailey 2 11 0.6513 0.3412
79 Min Choi 2 13 0.5500 0.3405
80 Alexander Santillan 0 13 0.5474 0.3389
81 Donald Park 0 8 0.6050 0.2305
82 William Sherman 0 8 0.5812 0.2214
83 David Spielman 0 8 0.5299 0.2019
84 Rahmatullah Sharifi 0 7 0.5769 0.1923
85 Kevin O'NEILL 0 6 0.6522 0.1863
86 Derrick Zantt 0 7 0.5385 0.1795
87 Michael Edmunds 0 4 0.6774 0.1290
88 Jamal Willis 0 3 0.5625 0.0804
88 TYREE BUNDY 0 3 0.5625 0.0804
90 Edward Ford 0 2 0.4375 0.0417
91 Carlos Caceres 0 1 0.6250 0.0298
92 Jason James 0 1 0.5625 0.0268
92 Michael Beck 0 1 0.5625 0.0268
94 Rodney Cathcart 0 1 0.5385 0.0256
95 Craig Webster 0 1 0.5000 0.0238

Data

---
title: "2023 NFL Moneyline Picks"
output: 
  flexdashboard::flex_dashboard:
    theme:
      version: 4
      bootswatch: spacelab
    orientation: rows
    vertical_layout: fill
    social: ["menu"]
    source_code: embed
    navbar:
      - { title: "Created by: Daniel Baller", icon: "fa-github", href: "https://github.com/danielpballer"  }
---


```{r setup, include=FALSE}
#    source_code: embed
library(flexdashboard)
library(tidyverse)
library(data.table)
library(formattable)
library(ggpubr)
library(ggrepel)
library(gt)
library(glue)
library(ggthemes)
library(hrbrthemes)
library(sparkline)
library(plotly)
library(htmlwidgets)
library(mdthemes)
library(ggtext)
library(ggnewscale)
library(DT)
source("./Functions/functions2.R")

thematic::thematic_rmd(font = "auto")

```

```{r Reading in our picks files, include=FALSE}
current_week = 21 #Set what week it is
week_1 = read_csv("./CSV_Data_Files/2023 NFL Week 1.csv")
week_2 = read_csv("./CSV_Data_Files/2023 NFL Week 2.csv")
week_3 = read_csv("./CSV_Data_Files/2023 NFL Week 3.csv")
week_4 = read_csv("./CSV_Data_Files/2023 NFL Week 4.csv")
week_5 = read_csv("./CSV_Data_Files/2023 NFL Week 5.csv")
week_6 = read_csv("./CSV_Data_Files/2023 NFL Week 6.csv")
week_7 = read_csv("./CSV_Data_Files/2023 NFL Week 7.csv")
week_8 = read_csv("./CSV_Data_Files/2023 NFL Week 8.csv")
week_9 = read_csv("./CSV_Data_Files/2023 NFL Week 9.csv")
week_10 = read_csv("./CSV_Data_Files/2023 NFL Week 10.csv")
week_11 = read_csv("./CSV_Data_Files/2023 NFL Week 11.csv")
week_12 = read_csv("./CSV_Data_Files/2023 NFL Week 12.csv")
week_13 = read_csv("./CSV_Data_Files/2023 NFL Week 13.csv")
week_14 = read_csv("./CSV_Data_Files/2023 NFL Week 14.csv")
week_15 = read_csv("./CSV_Data_Files/2023 NFL Week 15.csv")
week_16 = read_csv("./CSV_Data_Files/2023 NFL Week 16.csv")
week_17 = read_csv("./CSV_Data_Files/2023 NFL Week 17.csv")
week_18 = read_csv("./CSV_Data_Files/2023 NFL Week 18.csv")
week_19 = read_csv("./CSV_Data_Files/2023 NFL Wild Card.csv")
week_20 = read_csv("./CSV_Data_Files/2023 NFL Divisional Round.csv")
week_21 = read_csv("./CSV_Data_Files/2023 NFL Conference Round.csv")
# week_22 = read_csv("./CSV_Data_Files/2023 NFL Super Bowl.csv")

#reading in scores
Scores = read_csv(glue::glue("./CSV_Data_Files/NFL_Scores_{current_week}.csv")) 

#reading in CBS Prediction Records
cbs = read_csv(glue::glue("./CSV_Data_Files/CBS_Experts_{current_week}.csv")) %>% 
  mutate(Percent = round(Percent,4))
cbs_season = read_csv(glue::glue("./CSV_Data_Files/CBS_Experts_Season_{current_week}.csv"))

#reading in ESPN Prediction Records
espn = read_csv(glue::glue("./CSV_Data_Files/ESPN_Experts_{current_week}.csv"))%>% 
  mutate(Percent = round(Percent,4))
espn_season = read_csv(glue::glue("./CSV_Data_Files/ESPN_Experts_Season_{current_week}.csv"))%>% 
  mutate(Percent = round(Percent,4))

#Odds not working for the 2023 season.  Need to fix scrape code for next year.
#Reading in the moneyline odds for each team and cleaning the team names
# odds_wk1 = read_csv(glue::glue("./CSV_Data_Files/Moneyline_Odds_1.csv"))
# odds_wk2 = read_csv(glue::glue("./CSV_Data_Files/Moneyline_Odds_2.csv"))
# odds_wk3 = read_csv(glue::glue("./CSV_Data_Files/Moneyline_Odds_3.csv"))
# odds_wk4 = read_csv(glue::glue("./CSV_Data_Files/Moneyline_Odds_4.csv"))
# odds_wk5 = read_csv(glue::glue("./CSV_Data_Files/Moneyline_Odds_5.csv"))
# odds_wk6 = read_csv(glue::glue("./CSV_Data_Files/Moneyline_Odds_6.csv"))
# odds_wk7 = read_csv(glue::glue("./CSV_Data_Files/Moneyline_Odds_7.csv"))
# odds_wk8 = read_csv(glue::glue("./CSV_Data_Files/Moneyline_Odds_8.csv"))
# odds_wk9 = read_csv(glue::glue("./CSV_Data_Files/Moneyline_Odds_9.csv"))
# odds_wk10 = read_csv(glue::glue("./CSV_Data_Files/Moneyline_Odds_10.csv"))
# odds_wk11 = read_csv(glue::glue("./CSV_Data_Files/Moneyline_Odds_11.csv"))
# odds_wk12 = read_csv(glue::glue("./CSV_Data_Files/Moneyline_Odds_12.csv"))
# odds_wk13 = read_csv(glue::glue("./CSV_Data_Files/Moneyline_Odds_13.csv"))
# odds_wk14 = read_csv(glue::glue("./CSV_Data_Files/Moneyline_Odds_14.csv"))
# odds_wk15 = read_csv(glue::glue("./CSV_Data_Files/Moneyline_Odds_15.csv"))
# odds_wk16 = read_csv(glue::glue("./CSV_Data_Files/Moneyline_Odds_16.csv"))
# odds_wk17 = read_csv(glue::glue("./CSV_Data_Files/Moneyline_Odds_17.csv"))
# odds_wk18 = read_csv(glue::glue("./CSV_Data_Files/Moneyline_Odds_18.csv"))
# odds_wk19 = read_csv(glue::glue("./CSV_Data_Files/Moneyline_Odds_19.csv"))
# odds_wk20 = read_csv(glue::glue("./CSV_Data_Files/Moneyline_Odds_20.csv"))
# odds_wk21 = read_csv(glue::glue("./CSV_Data_Files/Moneyline_Odds_21.csv"))
# odds_wk22 = read_csv(glue::glue("./CSV_Data_Files/Moneyline_Odds_22.csv"))

####################UPDATE THESE###############################
inst.picks = list(week_1, week_2, week_3, week_4, week_5, week_6, week_7, week_8, week_9, week_10, week_11, week_12, week_13, week_14, week_15, week_16, week_17 , week_18, week_19 , week_20, week_21) #add in the additional weeks
# odds = rbind(odds_wk1, odds_wk2, odds_wk3, odds_wk4, odds_wk5, odds_wk6, odds_wk7, odds_wk8,
#              odds_wk9, odds_wk10, odds_wk11, odds_wk12) #add in the additional weeks
####################END OF UPDATE##############################

weeks = as.list(seq(1:current_week)) #creating a list of each week number
```

```{r read in scores clean data, include=FALSE}
#Cleaning Odds Data
# cl_odds = odds_cleaning(odds)

#Cleaning scores data
Scores = cleaning2(Scores)

#creating a list of winners for each week
winners = map(weeks, weekly_winners)

#creating a vector of this weeks winners
this_week = pull(winners[[length(winners)]])  

#Getting the number of games for each week
weekly_number_of_games = map_dbl(weeks, week_number_games)
```

```{r Group Predictions, include=FALSE}
#Creating the list of everyones predictions each week.
games = map(inst.picks, games_fn)

#Creating the prediction table.  
pred_table = map(games, pred_table_fn)

#Adding who won to the predictions
with_winners = map2(pred_table, winners, adding_winners)

#Creating results for each week.
results = map2(with_winners,weekly_number_of_games, results_fn)
```


```{r Displaying Group Results, echo=FALSE}
#Displaying the group results

inst_group_table = results[[length(results)]] %>% gt() %>% 
  cols_align(
    align = "center") %>% 
   tab_header(
    title = md("This Week's Predictions"),
    #subtitle = md(glue("Week {length(results)}"))
    ) %>% 
   tab_style(
    style = cell_text(color = "red", weight = "bold"),
    locations = cells_body(
      columns = c(Correct),
      rows = Correct =="No"
    )) %>% 
   tab_style(
    style = cell_text(color = "green", weight = "bold"),
    locations = cells_body(
      columns = c(Correct),
      rows = Correct =="Yes"
    )) %>% 
  tab_options(
    data_row.padding = px(3),
    container.height = "100%"
   )
```

```{r Weekly and season Group Results, include=FALSE}
# Printing the weekly and season win percentage     

#how many games correct, incorrect, and not picked each week
weekly_group_correct = map(results, weekly_group_correct_fn)  

#how many games were picked each week
weekly_games_picked = map2(weekly_group_correct, weekly_number_of_games, weekly_games_picked_fn)

#Calculating the number of correct picks for each week
weekly_group_correct_picks = map(weekly_group_correct, weekly_group_correct_picks_fn)

##### Remove this line before next season 
weekly_group_correct_picks[[21]]=0

#Calculating weekly win percentage
weekly_win_percentage = map2(weekly_group_correct_picks, weekly_games_picked, weekly_win_percentage_fn)

#Calculating season win percentage
season_win_percentage = round(sum(unlist(weekly_group_correct_picks))/sum(unlist(weekly_games_picked)),4)

#Calculating number of games picked this season
season_games = sum(unlist(weekly_games_picked))

#calculating season wins
season_wins = sum(unlist(weekly_group_correct_picks))

#calculating the number of people who picked this week
Total = dim(inst.picks[[length(weeks)]])[1]
```

```{r plotting group results, include=FALSE}
#Previous Weeks
group_season_for_plotting = unlist(weekly_win_percentage) %>% as.data.frame() %>% 
  rename(`Win Percentage` = ".") %>% 
  add_column(Week = unlist(weeks))
```

```{r Plotting the group results, echo=FALSE}
inst_group_season_plot = group_season_for_plotting %>% 
ggplot(aes(x = as.factor(Week), y = `Win Percentage`))+
  geom_point()+
  geom_path(aes(x = Week))+
  ylim(c(0, 1)) +
  xlab("NFL Week") + 
  ylab("Correct Percentage")+
  ggtitle("Weekly Group Correct Percentage")+
  theme_classic()+
  theme(plot.title = element_text(hjust = 0.5, size = 18))
```

```{r beating cbs week, include=FALSE}
#Creating a list of correct percentages for each week.
cbs_weekly_percent = map(weeks, cbs_percent)

#Creating a list of how many cbs experts we beat each week.
cbs_experts_beat = map2(cbs_weekly_percent, weekly_win_percentage, experts_beat)

#Creating a list of how many cbs experts picked each week.  
cbs_experts_total = map(cbs_weekly_percent, experts_tot)
```

```{r beating cbs season, include=FALSE}
#Creating a list of correct percentages for each week.
cbs_season_percent = map(weeks, cbs_season_percent)

#Creating a list of how many cbs experts we beat each week.
cbs_experts_beat_season = map2(cbs_season_percent, season_win_percentage, experts_beat)

#Creating a list of how many cbs experts picked each week.  
cbs_experts_season_total = map(cbs_season_percent, experts_tot)
```

```{r beating ESPN week, include=FALSE}
#Creating a list of correct percentages for each week.
espn_weekly_percent = map(weeks, espn_percent)

#Creating a list of how many cbs experts we beat each week.
espn_experts_beat = map2(espn_weekly_percent, weekly_win_percentage, experts_beat)

#Creating a list of how many cbs experts picked each week.  
espn_experts_total = map(espn_weekly_percent, experts_tot)
```

```{r beating ESPN season, include=FALSE}
#Creating a list of correct percentages for each week.
espn_season_percent = map(weeks, espn_season_percent)

#Creating a list of how many cbs experts we beat each week.
espn_experts_beat_season = map2(espn_season_percent, season_win_percentage, experts_beat)

#Creating a list of how many cbs experts picked each week.  
espn_experts_season_total = map(espn_season_percent, experts_tot)
```

```{r individual results, include=FALSE}
#Creating a list of individual results for each week.
weekly_indiv = pmap(list(inst.picks, winners, weeks), indiv_weekly_pred)

#Combining each week into one dataframe and calculating percentage Correct for this week.  
full_season = weekly_indiv %>% reduce(full_join, by = "Name") %>% 
  mutate(Percent = round(pull(.[,ncol(.)]/weekly_number_of_games[[length(weekly_number_of_games)]]),4)) 

#Creating a dataframe with only the weekly picks
a = full_season %>% select(starts_with("Week"))

#Creating a vector of how many weeks each person picked over the season
tot_week = NULL
help = NULL
for (i in 1:dim(a)[1]){
  for(j in 1:length(a)){
    help[j] = ifelse(is.na(a[i,j])==T,0,1)
    tot_week[i] = sum(help)
  }
}

#Creating a vector of how many games each person picked over the season
tot_picks= NULL
help = NULL
for (i in 1:dim(a)[1]){
  for(j in 1:length(a)){
    help[j] = unlist(weekly_games_picked)[j]*ifelse(is.na(a[i,j])==T,0,1)
    tot_picks[i] = sum(help)
  }
}

#Creatign a vector of how many games each person picked correct over the season
tot_correct = NULL
help = NULL
for (i in 1:dim(a)[1]){
  tot_correct[i] = sum(a[i,], na.rm = T)
}

#adding how many weeks each person picked, season correct percentage, and adjusted season percentag to the data frame and sorting the data
indiv_disp = full_season %>% add_column(`Weeks Picked` = tot_week) %>%
  add_column(tot_correct)%>%
  add_column(tot_picks)%>%
  mutate(`Season Percent` = round(tot_correct/tot_picks,4))%>%
  mutate(`Adj Season Percent` = round(`Season Percent`*(tot_week/length(a)),4)) %>%
  select(-tot_correct, -tot_picks) %>%
  arrange(desc(Percent), desc(`Season Percent`)) %>%
  mutate(Percent = ifelse(is.na(Percent)==T, 0, Percent))
```


```{r individual percentages, include=FALSE}
#Calculating individual percentages for each week.
weekly_indiv_percent = map2(weekly_indiv, as.list(weekly_number_of_games), indiv_percent) %>% reduce(full_join, by = "Name")

weekly_indiv_percent_plot = weekly_indiv_percent %>% 
  pivot_longer(cols = starts_with("Week"), names_to = "Week", values_to = "Percent")%>%
  mutate(Percent = ifelse(is.na(Percent)==T, 0, Percent)) %>% 
  mutate(Week = as.factor(Week))

levels = NULL
for(i in 1:length(weeks)){
  levels[i] = glue("Week {i}")  
}

weekly_indiv_percent_plot = weekly_indiv_percent_plot %>%
  mutate(Week = factor(Week, levels))
```

```{r sparklines, include=FALSE}
#adding sparklines
plot_group = function(name, df){
  plot_object = 
    ggplot(data = df,
           aes(x = as.factor(Week), y=Percent, group = 1))+
    geom_path(size = 7)+
    scale_y_continuous(limits = c(0,1))+
    theme_void()+
    theme(legend.position = "none")
  return(plot_object)
}

sparklines = 
  weekly_indiv_percent_plot %>% 
  group_by(Name) %>% 
  nest() %>% 
  mutate(plot = map2(Name, data, plot_group)) %>% 
  select(-data)
  
indiv_disp_2 = indiv_disp %>% 
  inner_join(sparklines, by = "Name") %>% 
  mutate(`Season Trend` = NA)
```

```{r Printing Individual Table2, echo=FALSE}
# Printing the individual Table
indiv_table = indiv_disp_2 %>% gt() %>% 
  cols_align(
    align = "center") %>% 
   tab_header(
    title = md("Individual Results"),
    subtitle = md(glue("Week {length(weeks)}"))
    ) %>% 
   tab_style(
    style = cell_text(color = "red", weight = "bold"),
    locations = cells_body(
      columns = c(Percent),
      rows = Percent<.5
    )) %>% 
   tab_style(
    style = cell_text(color = "green", weight = "bold"),
    locations = cells_body(
      columns = c(Percent),
      rows = Percent>.5
    )) %>% 
     tab_style(
    style = cell_text(color = "red", weight = "bold"),
    locations = cells_body(
      columns = c(`Season Percent`),
      rows = `Season Percent`<.5
    )) %>% 
   tab_style(
    style = cell_text(color = "green", weight = "bold"),
    locations = cells_body(
      columns = c(`Season Percent`),
      rows = `Season Percent`>.5
    ))%>% 
     tab_style(
    style = cell_text(color = "red", weight = "bold"),
    locations = cells_body(
      columns = c(`Adj Season Percent`),
      rows = `Adj Season Percent`<.5
    )) %>% 
   tab_style(
    style = cell_text(color = "green", weight = "bold"),
    locations = cells_body(
      columns = c(`Adj Season Percent`),
      rows = `Adj Season Percent`>.5
    )) %>% 
  tab_options(
    container.width = pct(100),
    data_row.padding = px(1),
    container.height = "100%"
   ) %>%
    tab_spanner(
    label = "Weekly # Correct",
    columns = starts_with(c("Week "))
  ) %>% 
  text_transform(
    locations = cells_body(c(`Season Trend`)),
    fn = function(x){
      map(indiv_disp_2$plot, ggplot_image, height = px(30), aspect_ratio = 4)
                 }) %>%
  cols_hide(c(plot))

indiv_winners = indiv_disp_2 %>% filter(Percent == max(Percent)) %>% select(Name) %>% pull() %>% paste(collapse = ", ")
indiv_season = indiv_disp_2 %>% filter(`Season Percent` == max(`Season Percent`)) %>% select(Name) %>% pull() %>% paste(collapse = ", ")
indiv_season_adj = indiv_disp_2 %>% filter(`Adj Season Percent` == max(`Adj Season Percent`)) %>% select(Name) %>% pull()%>% paste(collapse = ", ")
```

```{r Printing Season Leaderboard, echo=FALSE}
# Printing the Season Leaderboard
  
season_leaderboard = indiv_disp_2 %>% select(Name, starts_with("Week ")) %>% 
  pivot_longer(starts_with("Week"),names_to = "Week", values_to = "Correct") %>% 
  group_by(Week) %>% 
  mutate(Correct = case_when(is.na(Correct)==T~0, 
                             TRUE~Correct)) %>% 
  mutate(Donut = case_when(Correct==max(Correct)~1,
                           TRUE~0))  %>% 
  ungroup() %>% 
  group_by(Name) %>% 
  summarise(`Donuts Won` = sum(Donut)) %>% 
  #mutate(`Donuts Won` = strrep("award,", Donuts)) %>% 
  right_join(.,indiv_disp_2) %>% 
  select(-starts_with("Week "), -Percent) %>% 
  mutate(`Season Rank` = min_rank(desc(`Season Percent`)),.before = Name) %>% 
  arrange(`Season Rank`) %>% 
  gt() %>% 
  cols_align(
    align = "center") %>% 
   tab_header(
    title = md("Season Leaderboard (Season Percent)"),
    subtitle = md(glue("Week {length(weeks)}"))
    ) %>% 
  # fmt_icon(
  #   columns = `Donuts Won`,
  #   fill_color = "gold",
  # ) %>%
  tab_style(
    style = cell_text(color = "red", weight = "bold"),
    locations = cells_body(
      columns = c(`Season Percent`),
      rows = `Season Percent`<.5
    )) %>% 
   tab_style(
    style = cell_text(color = "green", weight = "bold"),
    locations = cells_body(
      columns = c(`Season Percent`),
      rows = `Season Percent`>.5
    ))%>% 
     tab_style(
    style = cell_text(color = "red", weight = "bold"),
    locations = cells_body(
      columns = c(`Adj Season Percent`),
      rows = `Adj Season Percent`<.5
    )) %>% 
   tab_style(
    style = cell_text(color = "green", weight = "bold"),
    locations = cells_body(
      columns = c(`Adj Season Percent`),
      rows = `Adj Season Percent`>.5
    )) %>% 
  tab_options(
    container.width = pct(100),
    data_row.padding = px(1),
    container.height = "100%"
   ) %>%
    tab_spanner(
    label = "Weekly # Correct",
    columns = starts_with(c("Week "))
  ) %>% 
  text_transform(
    locations = cells_body(c(`Season Trend`)),
    fn = function(x){
      map(indiv_disp_2$plot, ggplot_image, height = px(30), aspect_ratio = 4)
                 }) %>%
  cols_hide(columns = c(plot))
```

```{r Printing Adj Season Leaderboard, echo=FALSE}
# Printing the Adj Season Leaderboard
  
adj_season_leaderboard = indiv_disp_2 %>% select(Name, starts_with("Week ")) %>% 
  pivot_longer(starts_with("Week"),names_to = "Week", values_to = "Correct") %>% 
  group_by(Week) %>% 
  mutate(Correct = case_when(is.na(Correct)==T~0, 
                             TRUE~Correct)) %>% 
  mutate(Donut = case_when(Correct==max(Correct)~1,
                           TRUE~0))  %>% 
  ungroup() %>% 
  group_by(Name) %>% 
  summarise(`Donuts Won` = sum(Donut)) %>% 
  #mutate(`Donuts Won` = strrep("award,", Donuts)) %>% 
  right_join(.,indiv_disp_2) %>% 
  select(-starts_with("Week "), -Percent) %>% 
  mutate(`Season Rank` = min_rank(desc(`Adj Season Percent`)),.before = Name) %>% 
  arrange(`Season Rank`) %>% 
  gt() %>% 
  cols_align(
    align = "center") %>% 
   tab_header(
    title = md("Season Leaderboard (Adjusted Season Percent)"),
    subtitle = md(glue("Week {length(weeks)}"))
    ) %>% 
  # fmt_icon(
  #   columns = `Donuts Won`,
  #   fill_color = "gold",
  # ) %>%
  tab_style(
    style = cell_text(color = "red", weight = "bold"),
    locations = cells_body(
      columns = c(`Season Percent`),
      rows = `Season Percent`<.5
    )) %>% 
   tab_style(
    style = cell_text(color = "green", weight = "bold"),
    locations = cells_body(
      columns = c(`Season Percent`),
      rows = `Season Percent`>.5
    ))%>% 
     tab_style(
    style = cell_text(color = "red", weight = "bold"),
    locations = cells_body(
      columns = c(`Adj Season Percent`),
      rows = `Adj Season Percent`<.5
    )) %>% 
   tab_style(
    style = cell_text(color = "green", weight = "bold"),
    locations = cells_body(
      columns = c(`Adj Season Percent`),
      rows = `Adj Season Percent`>.5
    )) %>% 
  tab_options(
    container.width = pct(100),
    data_row.padding = px(1),
    container.height = "100%"
   ) %>%
    tab_spanner(
    label = "Weekly # Correct",
    columns = starts_with(c("Week "))
  ) %>% 
  text_transform(
    locations = cells_body(c(`Season Trend`)),
    fn = function(x){
      map(indiv_disp_2$plot, ggplot_image, height = px(30), aspect_ratio = 4)
                 }) %>%
  cols_hide(columns = c(plot))
```


```{r instructor formattable, echo=FALSE}
improvement_formatter <- 
  formatter("span", 
            style = x ~ formattable::style(
              font.weight = "bold", 
              color = ifelse(x > .5, "green", ifelse(x < .5, "red", "black"))),
             x ~ icontext(ifelse(x == max(x), "star", ""), x))

indiv_disp_3 = indiv_disp_2 %>% select(-plot)
indiv_disp_3$`Season Trend` = apply(indiv_disp_3[,2:(1+length(weeks))], 1, FUN = function(x) as.character(htmltools::as.tags(sparkline(as.numeric(x), type = "line", chartRangeMin = 0, chartRangeMax = 1, fillColor = "white"))))

indiv_table_2 = as.htmlwidget(formattable(indiv_disp_3, 
                                align = c("l", rep("c", NROW(indiv_disp_3)-1)),
              list(`Season Percent` = color_bar("#FA614B"),
              `Season Percent`= improvement_formatter,
              `Adj Season Percent`= improvement_formatter)))
              
indiv_table_2$dependencies = c(indiv_table_2$dependencies, htmlwidgets:::widget_dependencies("sparkline", "sparkline"))
```

```{r Plotting individual results over the season2, echo=FALSE, out.width = "100%"}
#Creating the individual plot.  
inst_indiv_plots = weekly_indiv_percent_plot %>% 
  ggplot(aes(x = factor(Week), y = Percent, color = Name))+
  geom_point()+
  geom_path(aes(x = as.factor(Week), y = Percent, color = Name, 
                group = Name))+
  ylim(c(0, 1)) +
  labs(x = "NFL Week", 
       y = "Correct Percentage", 
       title = "Weekly Individual Correct Percentage")+
  facet_wrap(~Name)+
  theme_classic()+
  theme(legend.position = "none",
        plot.title = element_text(hjust = 0.5, size = 18),
        axis.text.x=element_text(angle =45, vjust = 1, hjust = 1))
```

```{r data for data page}
inst.data = map2(inst.picks, weeks, disp_data) %>% bind_rows()
```


```{r fivethirtyeight}
inst_538 = map(results, five38) %>% unlist() %>% sum()
```

```{r pregame, eval=FALSE, include=FALSE}
#Predictions for the week

#Creating the list of group predictions each week.
games = map(inst.picks, games_fn)

#Creating the prediction table.  
pred_table = map(games, pred_table_fn)

#Printing table of instructor predictions
pred_table[[length(pred_table)]] %>% mutate(Game = row_number()) %>% 
  rename(`Votes For` = votes_for, `Votes Against` = votes_against) %>% 
  gt() %>% 
  cols_align(
    align = "center") %>% 
   tab_header(
    title = md("This Week's Predictions"),
    subtitle = md(glue("Week {length(weeks)}"))
    ) %>% 
   tab_options(
    data_row.padding = px(3)
   )
```

Group Predictions
==========================================================================

Sidebar {.sidebar} 
-------------------------------------
#### CBS Sports

<font size="4">

This week we beat or tied `r cbs_experts_beat[[length(weeks)]]` of `r cbs_experts_total[[length(weeks)]]` CBS Sports' Experts.

For the season we are currently beating or tied with `r cbs_experts_beat_season[[length(weeks)]]` of `r cbs_experts_season_total[[length(weeks)]]` CBS Sports' Experts.
 
 </font>


#### ESPN

<font size="4">

We also beat or tied `r espn_experts_beat[[length(weeks)]]` of `r espn_experts_total[[length(weeks)]]` ESPN Experts.
 
For the season we are currently beating or tied with `r espn_experts_beat_season[[length(weeks)]]` of `r espn_experts_season_total[[length(weeks)]]` ESPN Experts.

</font>

Row
--------------------------------------

### Win percentage for the week

```{r}
inst_rate <- weekly_win_percentage[[length(weekly_win_percentage)]]*100
gauge(inst_rate, min = 0, max = 100, symbol = '%', gaugeSectors(
  success = c(55, 100), warning = c(40, 54), danger = c(0, 39)
))
```

### Season Win Percentage

```{r}
inst_season <- season_win_percentage*100
gauge(inst_season, min = 0, max = 100, symbol = '%', gaugeSectors(
  success = c(55, 100), warning = c(40, 54), danger = c(0, 39)
))
```

### Games Correct
```{r}
valueBox(value = season_wins,icon = "fa-trophy",caption = "Correct Games this Season")
```

### Games Picked
```{r}
valueBox(value = season_games,icon = "fa-clipboard-list",caption = "Games Picked this Season")
```

### Number of predictions
```{r}
valueBox(value = Total,icon = "fa-users",caption = "Predictions this week")
```

Row
--------------------------------------

### 

```{r}
inst_group_table
```

### 

```{r}
ggplotly(inst_group_season_plot) %>% 
  layout(title = list(y = .93, xref = "plot"),
         margin = list(t = 40))
```

Individual Predictions
==========================================================================


Sidebar {.sidebar} 
-------------------------------------

#### Best Picks of the Week.

<font size="4">

 `r indiv_winners`
 
 </font>
 
#### Best Season Correct Percentage
<font size="4">

`r indiv_season`
 
 </font>

#### Best Adjusted Season Correct Percentage
<font size="4">

`r indiv_season_adj`

 * Adjusted season percentage accounts for the number of weeks picked.
 
 </font>

row {.tabset}
--------------------------------------

### Individual Table
```{r}
indiv_table
```

<!--
### Individual Table2

```{r, out.height="100%"}
indiv_table_2
```

-->

### Individual Plots
```{r, out.width="100%"}
ggplotly(inst_indiv_plots)
```

### Season Leaderboard
```{r, out.width="100%"}
season_leaderboard
```

### Adjusted Season Leaderboard
```{r, out.width="100%"}
adj_season_leaderboard
```

Data
==========================================================================

```{r}
datatable(
  inst.data, extensions = 'Buttons', options = list(
    dom = 'Blfrtip',
    buttons = c('copy', 'csv', 'excel', 'pdf', 'print'),
    lengthMenue = list( c(10, 25, 50, 100, -1), c(10, 25, 50, 100, "All") )
  )
)
```